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Deep learning-based facial image analysis in medical research: a systematic review protocol
Zhaohui Su1, Bin Liang2, Feng Shi3
1Center on Smart and Connected Health Technologies, Mays Cancer Center, School of Nursing, UT Health San Antonio, San Antonio, Texas, USA.
BMJ Open
|November 12, 2021
Summary
This systematic review will explore deep learning for facial image analysis in medicine. It aims to identify characteristics, challenges, and opportunities for disease detection, diagnosis, and prognosis.
Area of Science:
- Medical research
- Artificial intelligence
- Computer vision
Background:
- Deep learning (DL) shows promise in medical image analysis, outperforming humans in identification and classification tasks.
- Facial image analysis using DL is emerging for detecting medical conditions.
- A comprehensive understanding of the current state-of-the-art, challenges, and opportunities in medical DL facial image analysis is lacking.
Purpose of the Study:
- To systematically review the characteristics and effects of DL-based facial image analysis in medical research.
- To identify key challenges and opportunities in applying DL to facial image analysis for disease detection, diagnosis, and prognosis.
Main Methods:
- Systematic literature search of PubMed, PsycINFO, CINAHL, IEEEXplore, and Scopus (published in English, up to September 2021).
- Screening of titles, abstracts, and full-text articles, supplemented by manual reference list searches.
- Data extraction guided by study objectives and selection criteria, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework.
Main Results:
- This section will be populated upon completion of the systematic review.
Conclusions:
- This systematic review protocol outlines a comprehensive approach to understanding the landscape of DL-based facial image analysis in medicine.
- Findings will inform future research and clinical applications, focusing on patient welfare and practice development.
